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  3. TechCrunch AISelected

    Eka Robotics Completes $150 Million Series A Funding at a $1.45 Billion Valuation

    Eka Robotics has completed a $150 million Series A funding round led by a16z and F-Prime, at a post-money valuation of $1.45 billion. The company was founded by MIT professor Pulkit Agrawal and former Google DeepMind researcher Tuomas Haarnoja. Its vision-force-action model, paired with force-sensing grippers, can grasp about 1,500 objects weighing under 1 kg per hour, with a success rate exceeding 95%; the gripper costs less than $1,000 and is rated for 5 million cycles. The team uses a physics simulator to build reinforcement learning environments, and says it can generate thousands of hours of robot experience within minutes. It is currently running pilots with industrial customers for pick-and-place and cable manipulation.

    Eka Robotics has completed a $150 million Series A funding round led by a16z and F-Prime, at a post-money valuation of $1.45 billion. The company was founded by MIT professor Pulkit Agrawal and former Google DeepMind researcher Tuomas Haarnoja. Its vision-force-action model, paired with force-sensing grippers, can grasp about 1,500 objects weighing under 1 kg per hour, with a success rate exceeding 95%; the gripper costs less than $1,000 and is rated for 5 million cycles. The team uses a physics

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    #tech-media#Industry news#Embodied AI#English text is being prepared.
  17. Shao Meng

    MIT Course MAS.S62: Two Lectures on the Evolution of Harnesses and Multi-Agent Systems

    Lectures 4-5 of the MIT course MAS.S62 "How to AI an AI" focus on Harness evolution, covering the evolution of harness components, memory, tools, and skills, as well as the evolution of planning, task decomposition, and multi-agent systems. The memory section uses a three-axis taxonomy to connect MemGPT, mem0, MEM1, and Reflexion/ExpeL, and uses CLM to unify context management as file operations; the tools section moves from Toolformer, ToolLLM, and CodeAct to Meta-Harness, which represents the entire harness as a file system and provides about 400 times the amount of feedback information of previous methods. The multi-agent section presents five empirical findings, including that MAS wins 81% on decomposable tasks and loses 70% on tasks with sequential dependencies, and that CORAL achieves new SOTA on 8 tasks, with multi-agent evolution improving over single-agent by 4%-20%.

    Lectures 4-5 of the MIT course MAS.S62 "How to AI an AI" focus on Harness evolution, covering the evolution of harness components, memory, tools, and skills, as well as the evolution of planning, task decomposition, and multi-agent systems. The memory section uses a three-axis taxonomy to connect MemGPT, mem0, MEM1, and Reflexion/ExpeL, and uses CLM to unify context management as file operations; the tools section moves from Toolformer, ToolLLM, and CodeAct to Meta-Harness, which represents the

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    #hot-signal#Tutorials / practice#Agents#English text is being prepared.

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